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import numpy as np
from scipy.sparse import coo_matrix
from hypothesis import given, settings
import hypothesis.strategies as st
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
class TestSparseGradient(hu.HypothesisTestCase):
@given(M=st.integers(min_value=5, max_value=20),
N=st.integers(min_value=5, max_value=20),
K=st.integers(min_value=5, max_value=15),
sparsity=st.floats(min_value=0.1, max_value=1.0),
**hu.gcs_cpu_only)
@settings(deadline=10000)
def test_sparse_gradient(self, M, N, K, sparsity, gc, dc):
X = np.random.randn(M, K).astype(np.float32)
X[X > sparsity] = 0
X_coo = coo_matrix(X)
val, key, seg = X_coo.data, X_coo.col, X_coo.row
val = val.astype(np.float32)
key = key.astype(np.int64)
seg = seg.astype(np.int32)
Y = np.random.randn(K, N).astype(np.float32)
op = core.CreateOperator(
'SparseUnsortedSegmentWeightedSum',
['Y', 'val', 'key', 'seg'],
['out'],
num_segments=M)
# Gradient check wrt Y
self.assertGradientChecks(
gc, op, [Y, val, key, seg], 0, [0])
if __name__ == "__main__":
import unittest
unittest.main()
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